Benchmarks
Latest papers without code
GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra
We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms.
PSpan:Mining Frequent Subnets of Petri Nets
This paper proposes for the first time an algorithm PSpan for mining frequent complete subnets from a set of Petri nets.
An Efficient Graph Mining System for Large Patterns
In this work, we propose Angelica, a single-machine graph pattern mining system aiming at supporting large patterns.
Investigating ADR mechanisms with knowledge graph mining and explainable AI
We propose to mine knowledge graphs for identifying biomolecular features that may enable reproducing automatically expert classifications that distinguish drug causative or not for a given type of ADR.
Pattern Morphing for Efficient Graph Mining
Existing graph mining techniques including both custom graph mining applications and general-purpose graph mining systems, develop efficient execution plans to speed up the exploration of the given query patterns that represent subgraph structures of interest.
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers
For end-to-end efficiency, unlike previous work that assumes random hyperparameter tuning, which over-emphasizes the tuning time, we propose to evaluate with a bandit hyperparameter tuning strategy.
Mining Large Quasi-cliques with Quality Guarantees from Vertex Neighborhoods
In this work, we formally establish that two recurring characteristics of real-world graphs, namely heavy-tailed degree distributions and large clustering coefficients, imply the existence of substantially large vertex neighborhoods with high edge-density.
TopicBERT: A Transformer transfer learning based memory-graph approach for multimodal streaming social media topic detection
These properties of social networks which are known as 5'Vs of big data has led to many unique and enlightenment algorithms and techniques applied to large social networking datasets and data streams.
COMMUNITY DETECTION GRAPH MINING NAMED ENTITY RECOGNITION TRANSFER LEARNING
Adversarial Directed Graph Embedding
To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector.
Efficient and near-optimal algorithms for sampling connected subgraphs
(3) A near-optimal algorithm for $\epsilon$-uniform graphlet sampling.